class_docstr: Augmenter that simulate ocr error by random values. For example, OCR may recognize I as 1 incorrectly.        Pre-defined OCR mapping is leveraged to replace character by possible OCR error.
  
  :param float aug_char_p: Percentage of character (per token) will be augmented.
  :param int aug_char_min: Minimum number of character will be augmented.
  :param int aug_char_max: Maximum number of character will be augmented. If None is passed, number of augmentation is
      calculated via aup_char_p. If calculated result from aug_char_p is smaller than aug_char_max, will use calculated result
      from aup_char_p. Otherwise, using aug_max.
  :param float aug_word_p: Percentage of word will be augmented.
  :param int aug_word_min: Minimum number of word will be augmented.
  :param int aug_word_max: Maximum number of word will be augmented. If None is passed, number of augmentation is
      calculated via aup_word_p. If calculated result from aug_word_p is smaller than aug_word_max, will use calculated result
      from aug_word_p. Otherwise, using aug_max.
  :param int min_char: If word less than this value, do not draw word for augmentation
  :param list stopwords: List of words which will be skipped from augment operation.
  :param str stopwords_regex: Regular expression for matching words which will be skipped from augment operation.
  :param func tokenizer: Customize tokenization process
  :param func reverse_tokenizer: Customize reverse of tokenization process
  :param obj dict_of_path: Use pre-defined dictionary by default. Pass either file path of dict to use custom mapping. 
  :param str name: Name of this augmenter
  
  >>> import nlpaug.augmenter.char as nac
  >>> aug = nac.OcrAug()
code: def get_model(cls, dict_of_path):
      # Use default
      if not dict_of_path:
          default_path = os.path.join(
              LibraryUtil.get_res_dir(), "char", "ocr", "en.json"
          )
          model = ReadUtil.read_json(default_path)
          return nmc.Ocr(model=model)
  
      # Use dict
      if type(dict_of_path) is dict:
          return nmc.Ocr(model=dict_of_path)
  
      # Use json from file
      model = ReadUtil.read_json(dict_of_path)
      if not model:
          raise ValueError(
              'The dict_of_path does not exist. Please check "{}"'.format(
                  dict_of_path
              )
          )
      return nmc.Ocr(model=model)
  
file_path: nlpaug/nlpaug/augmenter/char/ocr.py
imports: import os
  from nlpaug.augmenter.char import CharAugmenter
  from nlpaug.util import Action, Method, Doc, ReadUtil, LibraryUtil
  import nlpaug.model.char
label_desc: Use image node_id 4 for calling the OcrAug obj's underlying member method code with example usage: obj.get_model(cls, dict_of_path) and returns: nmc, nmc, nmc